[1]} proposed an algorithm known as transfer learning embedded decision tree (TransEMDT) which integrates a decision tree and the k-means clustering algorithm to solve the cross-people activity recognition problem. Lin et al. [2]} identified a compact joint subspace for each class and then measured the distance between classes using principal angle. [3]} tried to learn more reliable pseudo labels using the majority voting technique on both domains. [4]} considered a special case that the new domain just contains the old one and [5]} proposed a heterogeneous transfer learning method for HAR. Recently, Qin et al. [6]} proposed an adaptive spatial-temporal transfer learning (ASTTL) approach to select the most similar source domain to the target domain and accurately transfer activity. Despite many approaches have been designed to solve the CDAR problem and some of them attempted to use clustering methods, little work is substructure-based. SOT tries to complete substructure-level matching through joint Gaussian Mixture Model and optimal transport. The number of substructures may be different from the number of the classes.
